The present disclosure relates to a method and system for evaluating parameters employed for manufacturing semiconductor structures, and particularly to a method for comparing and/or selecting a set of lithographic processing conditions and/or data preparation processes for printing a lithographic pattern, a system for performing the same, and a non-transitory machine-readable data storage device embodying a program for performing the same.
Lithographic capabilities are one of the significant technological limitations that constrain the continued scaling of semiconductor devices. In order to manufacture high performance semiconductor devices at a high yield, a lithographic pattern must be printed without triggering functional failures in semiconductor devices while providing minimum lithographic dimensions where necessary.
The complexity in interference patterns during lithographic printing can be simulated for a given set of lithographic processing conditions and a given lithographic mask. Further, the pattern in a lithographic mask can be adjusted by setting data preparation parameters during data preparation. Thus, simulation of a physical lithographic pattern is possible even before manufacturing a lithographic mask by providing data preparation parameters to be employed to manufacture a lithographic mask and a set of lithographic processing conditions on the lithographic mask to be employed at the lithographic processing step, e.g., parameters relating to the photoresist to be employed and lithographic exposure conditions to be employed.
A simulated lithographic pattern can be checked to determine whether any feature size therein is too small for achieving a reasonable level of yield during manufacturing of corresponding semiconductor devices. A program that encodes rules for ideal simulated lithographic patterns is referred to as an “optical rule checker,” i.e., an “ORC.” An ORC can be run to identify regions of a simulated lithographic pattern generated under the assumption of a selected set of lithographic processing conditions and a selected set of data preparation conditions, or under the assumption of a selected set of lithographic processing conditions and a particular lithographic mask manufactured employing a selected set of data preparation conditions. A violation of the ORC rule can occur either by a pattern that produces a line width (i.e., a width of a region of a photoresist as simulated) that is less than a minimum line width or a pattern that produces a spacing (i.e., a distance between two adjacent photoresist portions as simulated) that is less than a minimum spacing.
Two prior art methods are known for evaluation of lithographic processing conditions and data preparation parameters employing an ORC. A first method is manual observation and comparison of regions that violate rules of the ORC. This method tends to require excessive time and effort for a complex lithographic pattern that is typically present in large scale integrated semiconductor devices.
A second method is an automated categorized counting of features in the simulated lithographic pattern. In the second method, the degree of failure under the ORC rules is categorized according to the degree of failure. For example, a feature that provides a minimum width of 25 nm and a feature that provides a minimum width of 30 nm where the ORC rules require a minimum width of 32 nm can be classified as failures of two different degrees, in which one feature fails by 7 nm and another feature fails by 2 nm. The different degrees of failure can be characterized by a histogram including multiple failure “buckets,” in which each bucket represents a non-overlapping range of deviations from the minimum width required under the ORC rules. In the example illustrated above, the feature that fails by 7 nm increases a failure count in a failure bucket that includes 7 nm in the range, and the feature that fails by 2 nm by a failure count in a failure bucket that includes 2 nm in the range. In general, the second method provides a histogram of a predefined failure buckets for each combination of data preparation parameters and lithographic processing conditions.
While the second method provides an automated method of comparing data preparation parameters and/or lithographic processing conditions that can be employed for a lithographic pattern, comparison of data preparation parameters and/or lithographic processing conditions employing the second method is difficult because one histogram must be compared with another histogram. Histograms can be difficult to compare in selecting an optimal set of data preparation parameters and/or an optimal set of lithographic processing conditions because multiple parameters, i.e., the counts in each bucket, are involved in the comparison.
Furthermore, methods of counting discrete errors cannot be used in combination with certain forms of automated optimization algorithms, which are desirable as a means of automatically computing optimum lithography or data preparation parameters. For example, Levenberg-Marquardt optimization requires a continuous error measure in order to function properly, and as such, cannot effectively employ data in the form of a histogram for optimization purposes.